Conference Agenda

Surface radiation balance is a very important energy source in study of the third pole region ’s evapotranspiration, snow and glacier melting. It is a controlling factor in characterizing the regional energy and water cycle’s system and it’s change. However, all currently available radiation products in this area are not suitable for regional scale study of water and energy exchange and snow/glacier melting due to their coarse resolution and low accuracies, such as the re-analyses data. The study summarizes our recent progress on the all-sky surface radiation estimation with high spatial-temporal resolution remote sensing techniques. The significant improvement of these products is the full consideration of the effect of clouds and topography on derived radiation. Our goal is to produce high-resolution (< 2km, half-hour) short- and long-wave radiation (downward and net components) to drive high-resolution hydrological model’s application and to improve our understanding the third pole region’s energy and water cycle’s system.

Glacier-climate interaction and its spatial variability over the Tibetan Plateau is still poorly understood. We present a new distributed glacier mass balance model applied on two glaciers of the Tibetan Plateau: Parlung No. 4 Glacier, 11.7 km², a temperate-maritime glacier, and Zhadang Glacier, 2.0 km², a sub-continental glacier. Enthalpy, rather than temperature, is used in the energy budget equations to simplify the computation of latent heat fluxes from water phase changes and the movement of liquid water in the snow. Two novel methods are used to distribute near-surface air temperature and wind speed from a set of Automatic Weather Stations (AWS). Further, we apply a new method to discriminate between solid and liquid precipitation based on daily mean air temperature, relative humidity, and elevation. Model results are evaluated by in-situ mass balance observations of the Parlung No. 4 Glacier and remote sensing products.

Our aims are to: i) develop a novel enthalpy-based model and test its performance on the distributed simulations of glacier mass balance and energy budget; ii) compare the physical processes typical of the summer season on two different types of glaciers on the Tibetan Plateau; iii) identify the key model sensitivities at both study sites. We present the interplay of precipitation thresholds, albedo and net radiation at these different glaciers and discuss their implications for future mass balance modelling on the Tibetan Plateau.

The response of glaciers to climate in the high-elevation Tibetan Plateau (TP) is generally poorly understood and is highly variable in space and time. A key influence on glaciers of the TP and surrounding mountain ranges is the monsoon, which for a large majority of TP glaciers overlaps with the main melting season and determines a very specific regime of mixed accumulation and ablation in summer. Monsoon effects on glacier mass balance however are still little understood. We use a distributed energy balance model, combined with high-resolution meteorological observations and new schemes for precipitation discrimination and albedo evolution to understand the effect of rain events and monsoon precipitation on the summer mass balance of a monsoon-dominated glacier of the TP. The main effect of precipitation events is to considerably alter surface conditions, maintaining higher reflectivity surface for most of the season. We show that it is challenging to reproduce this effect with traditional approaches based on simple discrimination of solid/liquid precipitation. The glacier summer mass balance is highly sensitive to precipitation thresholds discriminating between rain, sleet or snow. Precipitation acts both on the actual mass balance as well as the surface albedo. Adjustment of albedo during sleet events is crucial to correctly reproduce the glacier mass balance, and neglecting it leads to much higher mass losses and more negative mass balance over the entire glacier but especially at higher elevations, with a similar negative impact on summer mass balance than prescribing ~69% less snow accumulation for the upper-glacier. Based on static air temperature shifts of +1.5°C, it is found that the dynamic precipitation discrimination approach based on wet bulb temperatures results in a monsoon period mass balance up to 36% more sensitive than if assuming a single value threshold for solid and liquid precipitation. Our work identifies a key and complex role of precipitation events on the glacier mass balance, and a strong need for improving the modelling of local precipitation gradients and thresholds based on observations of a high spatio-temporal resolution.

Hydrologic model is a simplification of a real-world system that aids in understanding, predicting, and managing regional water resources. The quality of driving data greatly influences the accuracy of model simulation. Red River a China-Laos-Vietnam transboundary river. The upstream and middle stream of which are dry-hot valley regions with large altitude difference(1893-1916). The water resources simulation and management are difficult and complex. The new version of Chinese Land Data Assimilation Systems(CLDAS-V2) integrated advantages of point-based ground meteorological observations and remote sensing products. The products have higher 6km spatial resolution and higher quality within the China boundary. In this study, we simulated the soil moisture and runoff in Red River Basin(RRB) in 2017-2018 by using The Variable Infiltration Capacity (VIC) model based on CLDAS-V2.0 products, as well as state data (e.g. 250m DEM, MODIS 500m LAI products). The prelimary result show that the daily runoff simulation fits well with actual runoff observation in Yuanjiang station and Tukahe station in early 2018. There are several big rivers derived from Asia high plain. This study reveals the usefulness of CLDAS-V2 product in similar transboundary river basin for flood and drought management. There are good water level-runoff regression relation in RRB. Our results will be validated with water level of small water body by SAR altimetry and 1km spatial resolution downscaled SMOS Soil Moisture products[Gao et al. 2018].

Water balance in red river basin is very complex. Due to complex topography, total drop of red river is high (2574m). One of the greatest challenges for flood prediction and integrated water management in the Red River basin is a lack of information on reservoir management, as a consequence, it is not easy to estimate the water resources. Since it is a transboundary river, there are difficulties to manage the area as a whole, and the information might not be in time for flood and drought early warning.
Thanks to Microwave Remote Sensing, water resources can be monitored within any weather conditions remotely. The main objective of this project is to develop the algorithms and synergies between different Microwave Remote Sensing sensors to be able to monitor water resources in the Red River Basin. Accounting with water level products over Redriver basin, soil moisture products and runoff, can help improving water resource management in this area.
SAR altimetry with the slicing of the altimeter footprint that the SAR mode performs, allows the separate computation of all the elementary reflectors, in particular the wet ones. In this project, we took advantage of SAR altimetry to monitor small water bodies in Red River basin.
However, satellite altimeters are initially designed to monitor homogeneous surfaces such as oceans or ice sheets, which results in poor performance over small inland water bodies because of the land contamination contribution in the returned waveforms. To improve altimeter range accuracy which relates to water level measurement accuracy, the waveform needs to be retracked precisely to find out the accurate tracking point which locates on the leading edge [Deng and Featherstone, 2006]. In our study, we use the Sentinel-3 satellite with data available from June 2016 to retrieve the water level in Red river basin. The new retrackers [Makhoul e al., 2018] including the threshold, OCOG, and 2-step analytical retracker are introduced to find the tracking points precisely from the land contaminated waveforms. The waveform portion selection is applied for the water level retrieval using DEM information to locate the waveform portion which comes from nadir.
The aim of the study is to retrack water level more precisely by comparing the threshold, OCOG and 2-step analytical retracker performance along with the waveform portion selection approach performance over Red river. It is a challenging task since the water bodies are very small, the widths of which we studied are mostly around 200m to 500m, which means there are only one or two signals in the water for Sentinel-3 whose spatial resolution is 300m along track. For Redriver basin, Sentinel-3 works in closed loop tracking, which means the altimeter range window is autonomously positioned based on on-board NRT analysis of previous SRAL waveform. With the mountainous topography, it is more challenging to locate the leading-edge position of the waveform.
The water level results of the three retrackers with waveform portion selection are then compared with the onboard ocean retracker. Over places with land contamination, the analytical 2-step retracker with waveform portion selection has shown better performances than ESA L2.
The preliminary results show a smoothly time series over several water bodies in Red river basin, and waveform portion selection works much better than using the whole waveform since the DEM information is introduced to eliminate land contamination. Retracking water level from Level-1 waveforms using the combination of the retrackers and waveform portion selection is more robust to land contamination than using Level-2 data directly.
We will be producing Soil Moisture products at 1km spatial resolution for the entire Red River basin for the 2010-2017 period. The Soil Moisture products will be SMOS based and we will use the DisPATCh disaggregation scheme to increase SMOS coarse resolution. DisPATCh provides 1 km resolution SM data from coarse-scale microwave derived SM. In DisPATCh, the soil evaporation from the 0-5 cm soil layer and the vegetation transpiration from the root zone soil layer are partitioned by separating MODIS LST (Land Surface Temperature) into its soil and vegetation components. The partitioning method relies on a contextual interpretation of MODIS LST and MODIS NDVI [Moran et al. 1994]. MODIS-derived soil temperature is first used to estimate Soil Evaporative Efficiency (SEE defined as a ratio of actual to potential soil evaporation), which is known to be relatively constant during the day on clear sky conditions. DisPATCh then distributes high-resolution soil moisture around the low-resolution observed mean value using the instantaneous spatial link between optical-derived SEE and near-surface soil moisture [Merlin et al. 2013].
These soil moisture estimates will be validated against output from modeling [Rui et al. 2018].
With water level products and soil moisture products over Redriver basin, the hydrological model for water resource estimation can be improved in future steps.

Abstract:

Soil moisture (SM) is a significant determinant of crop growth and a useful indicator of drought. It is important to evaluate and analyze existing soil moisture products for environmental monitoring and protection of the Belt and Road region. At present, there are many global soil moisture products, such as the ones retrieved from the data collected by AMSR-E (the Advanced Microwave Scanning Radiometer-Earth Observing System), AMSR-2(the Advanced Microwave Scanning Radiometer 2), FY3B/C (the Feng Yun 3rd Satellite), LPDR (the Daily Global Land Parameters Derived from AMSR-E andAMSR-2) and ESA CCI (the ESA Climate Change Initiative) at a coarse resolution of ~0.25◦.

In this study, the 8 soil moisture products were selected (AMSR-E/JAXA, AMSR-E/NASA, AMSR-2/JAXA, AMSR-2/NASA, FY3B/C, LPDR and ESA CCI). The approximate ascending and descending equator crossing time, channel and incident angle, except LPDR and ESA CCI are indicated. Among them, the LPDR product is derived from other three soil moisture products (AMSR-E, AMSR-2 and FY3B). LPDR soil moisture product was developed by using the double difference and inter-calibration methods from AMSR-2, AMSR-E and FY3B. The ESA CCI product was developed by merging many passive and active soil moisture products, such as AMSR-2, SMOS, MetOp-A and so on. In this study, the JAXA and NASA soil moisture products AMSR-E and AMSR-2 were selected. The overlapping time of AMSR-E, FY3B, LPDR and EAS CCI is 2011. The overlapping time of AMSR-2, FY3B/C, LPDR and EAS CCI is from 2014 to 2016. According to the overlapping time of soil moisture products, the comparison and validation of different soil moisture products was supported with in-situ data from ISMN (the International Soil Moisture Network) and ERA Interim/Land (0-7cm soil depth). Secondly, soil moisture content is influenced by various factors, such as soil type, land-use type, climate type and so on. The climate type implies patterns in rainfall and temperature that affect the retrievals, but also closely related to surface types. These effect factors also influence the soil moisture content. Therefore, in this study, the climate type is introduced in soil moisture product analysis at the Belt and Road region.

Keywords— soil moisture product, Belt and Road, comparison, validation

The Earth Observation (EO) mission for mapping global surface soil moisture and generating related satellite products have been witnessed a great progress in the last several decades. Among several global soil moisture products, the soil moisture products developed based on the European Space Agency Climate Change Initiative (ESA CCI) are the most complete and longest temporal serial soil moisture data records.

The latest versions (v04.2 v03.3) of CCI soil moisture products were released on Jan. 17, 2018 and Nov. 27, 2017, respectively. These two versions of the products cover the temporal range from October of 1978 to the end pf 2016. The previous versions of the products have been intensively validated. However, the evaluation of the latest version has not been reported yet. The main aim of this study is to provide an in-deep evaluation of the latest CCI soil moisture products using ground observations. To this end, ground observation from three soil moisture observation networks distributed in Tibetan Plateau, namely BBHNet, MAQU and CTP-SMTMN, are used as the reference data. The results show that the products present a little underestimation of the soil moisture over the three regions. But both versions of the products show good agreement with the temporal variation of the ground observations. Relatively, the v03.3 product is a little better than the v04.2 product.

Glaciers in the Tibetan Plateau are important climate indicators due to their rapid response to climate variability. Therefore, it is crucial to understand glacier changes and their response to climate change. Long-term series of satellite data can provide such information. The complexity of observing and understanding changes in glacier conditions is augmented by the spatial heterogeneity of the glacier surface. Automatic glacier mapping utilizing remote sensing data is even more challenging due to the spectral similarity of supraglacial debris and the adjacent bedrock, orographic clouds and highly variable snow conditions. The vast majority of the available glacier datasets only provide the total glacier area, which means that the boundary between clean ice and debris-covered glacier is not clear. Different glacier elements have different melt rates and densities. This discrimination plays a key role in mass balance research and improved hydrological modeling.

The aim of this study was to distinguish ice cover types on a given date in a subregion of the Parlung Zangbo basin in the southeastern Tibetan Plateau. Multitemporal analyses will be dealt with in a later study. The classification was carried out by employing an automated machine learning approach – Random Forests in combination with the analysis of topographic and textural features based on Landsat-8 image and ASTER GDEM data. The Gao Fen-1 (GF-1) PMS image was used to validate classification results. In this study, all the glacierized terrain types were classified with very high overall accuracy (>98%). The results indicated that debris-covered glaciers accounted for approximately 15.86% of the total glacier area in this region and debris covered glaciers were mainly distributed between 4600 m and 4800 m a.s.l. Additionally, analysis of the results clearly revealed that the number proportion of small glaciers (<1 km²) was 92.18%, which were distributed at lower elevation than large glaciers. In future work, the recognition of debris-free and debris-covered glaciers require further studies with more field observations and higher resolution DEM dataset.

Keywords: Automatic glacier mapping; Random Forests; Landsat; Parlung Zangbo basin